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E-raamat: Fuzzy Systems in Bioinformatics and Computational Biology

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Biological systems are inherently stochastic and uncertain. This book shows how fuzzy logic, a powerful tool in capturing uncertainties in engineering systems, has in recent years become popular in analyzing biological data and modeling biological systems.



Biological systems are inherently stochastic and uncertain. Thus, research in bioinformatics, biomedical engineering and computational biology has to deal with a large amount of uncertainties.

Fuzzy logic has shown to be a powerful tool in capturing different uncertainties in engineering systems. In recent years, fuzzy logic based modeling and analysis approaches are also becoming popular in analyzing biological data and modeling biological systems. Numerous research and application results have been reported that demonstrated the effectiveness of fuzzy logic in solving a wide range of biological problems found in bioinformatics, biomedical engineering, and computational biology.

Contributed by leading experts world-wide, this edited book contains 16 chapters presenting representative research results on the application of fuzzy systems to genome sequence assembly, gene expression analysis, promoter analysis, cis-regulation logic analysis and synthesis, reconstruction of genetic and cellular networks, as well as biomedical problems, such as medical image processing, electrocardiogram data classification and anesthesia monitoring and control. This volume is a valuable reference for researchers, practitioners, as well as graduate students working in the field of bioinformatics, biomedical engineering and computational biology.

Induction of Fuzzy Rules by Means of Artificial Immune Systems in
Bioinformatics.- Fuzzy Genome Sequence Assembly for Single and Environmental
Genomes.- A Hybrid Promoter Analysis Methodology for Prokaryotic Genomes.-
Fuzzy Vector Filters for cDNA Microarray Image Processing.- Microarray Data
Analysis Using Fuzzy Clustering Algorithms.- Fuzzy Patterns and GCS Networks
to Clustering Gene Expression Data.- Gene Expression Analysis by Fuzzy and
Hybrid Fuzzy Classification.- Detecting Gene Regulatory Networks from
Microarray Data Using Fuzzy Logic.- Fuzzy System Methods in Modeling Gene
Expression and Analyzing Protein Networks.- Evolving a Fuzzy Rulebase to
Model Gene Expression.- Infer Genetic/Transcriptional Regulatory Networks by
Recognition of Microarray Gene Expression Patterns Using Adaptive Neuro-Fuzzy
Inference Systems.- Scalable Dynamic Fuzzy Biomolecular Network Models for
Large Scale Biology.- Fuzzy C-Means Techniques for Medical Image
Segmentation.- Monitoring and Control of Anesthesia Using Multivariable
Self-Organizing Fuzzy Logic Structure.- Interval Type-2 Fuzzy System for ECG
Arrhythmic Classification.- Fuzzy Logic in Evolving in silico Oscillatory
Dynamics for Gene Regulatory Networks.